UID:
almahu_9947917145302882
Format:
XX, 201 p. 90 illus., 29 illus. in color.
,
online resource.
ISBN:
9783658205409
Content:
This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun’s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch.
Note:
Approaches to Unsupervised Machine Learning -- Methods of Visualization of High-Dimensional Data -- Quality Assessments of Visualizations -- Behavior-Based Systems in Data Science -- Databionic Swarm (DBS).
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9783658205393
Language:
English
Keywords:
Electronic books.
;
Electronic books.
DOI:
10.1007/978-3-658-20540-9
URL:
http://dx.doi.org/10.1007/978-3-658-20540-9
URL:
ProQuest Ebook Central
URL:
Click here to view book
Bookmarklink